Rescuing error control in crosslinking mass spectrometry.

Crosslinking mass spectrometry is a powerful tool to study protein-protein interactions under native or near-native conditions in complex mixtures. Through novel search controls, we show how biassing results towards likely correct proteins can subtly undermine error estimation of crosslinks, with significant consequences. Without adjustments to address this issue, we have misidentified an average of 260 interspecies protein-protein interactions across 16 analyses in which we synthetically mixed data of different species, misleadingly suggesting profound biological connections that do not exist. We also demonstrate how data analysis procedures can be tested and refined to restore the integrity of the decoy-false positive relationship, a crucial element for reliably identifying protein-protein interactions. Synopsis: Accurate error estimation in crosslinking mass spectrometry (MS) is crucial for uncovering new biology. However, new data analysis methods may disrupt the decoy-false positive relationship that is at the basis of error estimation. This study offers tests and remedies to address this issue. Crosslinking MS error control easily breaks, obscuring true errors and inflating novel biology claims. Concatenated data and true positive estimation reveal data processing errors. Pairing targets and decoys throughout the analysis improves the robustness of error estimation. Accurate error estimation in crosslinking mass spectrometry (MS) is crucial for uncovering new biology. However, new data analysis methods may disrupt the decoy-false positive relationship that is at the basis of error estimation. This study offers tests and remedies to address this issue. [ABSTRACT FROM AUTHOR]